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Chainguard Workspaces overview

Thu, 08 Oct 2026 00:00:00 +0000

Chainguard Workspaces moves development, and the coding agents that do it, off your laptop. The chainctl develop command opens a session: a Linux microVM in Chainguard’s cloud with your repository checked out, the tools your repository needs, and, by default, Claude Code. You work in it from your own terminal.

A coding agent that runs on your laptop can read your SSH keys, cloud credentials, and browser sessions, and every package it installs runs with the same access. In a session, the agent works in a separate VM that holds no long-lived credentials. When the agent pushes to GitHub or calls Claude, the request leaves the VM without a credential, and your laptop or Chainguard adds one on the way out.

Guardener Dockerfile migration

Mon, 13 Jul 2026 00:00:00 +0000

The Dockerfile migration feature converts your Dockerfiles to use Chainguard Containers. It uses AI to iteratively translate instructions, build images, compare results, and fix issues until the migrated Dockerfile works as expected.

Unlike Guardener’s Hardened Actions and Commit Verification features, Dockerfile migration does not run through the Chainguard App or the .chainguard/ configuration directory. Instead, you drive it locally through chainctl agent dockerfile commands. The AI runs server-side and scans your workspace to perform its analysis, while Docker builds and file access remain local to your machine.

Getting started with the PyTorch Chainguard Container

Thu, 25 Apr 2024 08:00:00 +0200

Chainguard’s PyTorch container image provides a security-hardened foundation for deep learning workloads. Built with PyTorch and CUDA support for GPU acceleration, this minimal image maintains full deep learning capabilities while reducing attack surface. This guide demonstrates fine-tuning models and secure inference deployment.

What is Deep Learning?

Deep learning is a subset of machine learning that leverages a flexible computational architecture, the neural network, to address a wide variety of tasks. Neural networks emulate the structure of the brain and consist of interconnected nodes (neurons) that each contain an associated weight and threshold. In concert with an activation function, these values determine whether data is propagated within the network, producing an output layer corresponding to a classification, regression, or other result.

By technical convention, a deep neural network (DNN) has at least three layers: an input layer, an output layer, and one or more hidden layers. In practice, DNNs often have many layers.

Deep neural networks underpin many common computational tasks in modern applications, such as speech to text and generative AI.

Use Claude Code in a Chainguard Workspaces session

Thu, 08 Oct 2026 00:00:00 +0000

A Chainguard Workspaces session is a place for a coding agent to work with less risk to your laptop. The agent runs in the session’s VM, which holds no long-lived credentials, and it reaches Claude with your own Claude credential, which stays outside the VM. This page explains how to run Claude Code in a session.

Claude Code is the only coding agent that sessions supply a model credential for. You can install other coding agents in a session, but sessions don’t send them a credential.

AI with hardened container images

Thu, 24 Jul 2025 17:00:00 +0000

The July 2025 Learning Lab with Patrick Smyth covers AI with Hardened Container Images. In this session, learn how to secure AI workloads by reducing vulnerabilities in container images by over 90%. Patrick demonstrates hands-on techniques for training an animal detection model using PyTorch with hardened container images, creating minimal and secure deployments, and running AI frameworks with zero CVEs.

Sections

0:00 Introduction and updates
2:02 Preparation: Docker pull instructions for demo
3:39 Chainguard! Who are we?
4:34 CVE system fundamentals
6:48 “Boss assigned me to fix Ubuntu” problem
7:41 Introduction to Chainguard Containers
8:54 Zero CVE containers: Real results and comparisons
11:10 How we achieve zero CVEs: Minimal, Fresh, Advisory, Patch
13:24 AI container challenges: Size and complexity
14:59 PyTorch container analysis: CVEs, packages, and executables
16:21 Demo introduction: Image classification with PyTorch
17:59 Demo walkthrough and repository overview
19:28 Demo: Running the training command
22:01 Demo: Downloading test image and running inference
23:20 Recent developments in Chainguard AI containers
25:09 Other AI containers: TensorFlow, KServe, Triton backends
26:46 Q&A
35:18 Chainguard AI course and additional resources

Demo

In the demo, Patrick trains and runs inference on an image classification model using PyTorch and Chainguard’s hardened container image. The model classifies images of octopuses, whales, and penguins, demonstrating how to work with AI workloads securely.